PROFILING RESIDENTIAL WATER USERS’ ROUTINES BY EIGENBEHAVIOR MODELLING
1. PROFILING RESIDENTIAL WATER USERS’ ROUTINES
BY EIGENBEHAVIOR MODELLING
Andrea Cominola, AndreaMoro, Luca Riva, Matteo Giuliani, AndreaCastelletti
2. RESIDENTIAL URBANWATER MANAGEMENT
. Demand-side management
Long term
water security
Source: United Nations. Department of Economic and Social Affairs. Population Division, 2010
Leflaive, X., et al. (2012), "Water", in OECD, OECD Environmental Outlook to 2050: The Consequences of Inaction, OECD Publishing, Paris.
2000 2030 2050
+130%
Domesticwater
demand
41 megacities
worldwide
Short term
costs
Interventionsduring
low recharge periods
3. RESIDENTIAL URBANWATER MANAGEMENT
. Demand-side management
. Smart metering
1990
1994
50
30
10
1995
1999
2000
2004
2005
2009
2010
2015
134 studies over the last
25 years
Contents lists available at ScienceDirect
Environmental Modelling & Software
journal homepage: www.elsevier.com/locate/envsoft
Environmental Modelling & Software 72 (2015) 198e214
Benefits and challenges of using smart meters for advancing
residential water demand modeling and management: A review
A. Cominola a
, M. Giuliani a
, D. Piga b
, A. Castelletti a, c, *
, A.E. Rizzoli d
a
Contents lists available at ScienceDirect
Environmental Modelling & Software
journal homepage: www.elsevier.com/locate/envsoft
Environmental Modelling & Software 72 (2015) 198e214
5. RESIDENTIAL URBANWATER MANAGEMENT
. Demand-side management
. Smart metering
. Water consumers
CUSTOMIZED DEMAND SIDEMANAGEMENT
5% reduction through
customized social norms
6. CHALLENGES
. Smart metered BIG DATA
. How to extract RELEVANTUSERS PROFILES out of large smart
metered datasets?
. How to characterize these profiles, in order to inform
DEMANDMANAGEMENT?
14. EIGENBEHAVIOR EXTRACTION
SMART METERED
DATA
H (hour of day)
DATA
LABELING
D(days)
H (hour of day)
D(days)
Label 0
Label 1
Label 2
Label 3
0.0
0.1
0.2
0.3
0.4
0.5
L0 L1 L2 L3
%ofreadings
LO = 0 L/h
L1 = (0,12] L/h
L2 = (12,100] L/h
L3 > 100 L/h
15. EIGENBEHAVIOR EXTRACTION
SMART METERED
DATA
H (hour of day)
DATA
LABELING
D(days)
H (hour of day)
D(days)
Label 0
Label 1
Label 2
Label 3
BINARY TRANSFORMATION
H (hour of day)
D(days)
H (hour of day) H (hour of day)
Label 1 Label 2 Label 3
H (hour of day)
Label 0
16. EIGENBEHAVIOR EXTRACTION
SMART METERED
DATA
H (hour of day)
DATA
LABELING
D(days)
H (hour of day)
D(days)
Label 0
Label 1
Label 2
Label 3
BINARY TRANSFORMATION
D(days)
PRINCIPAL COMPONENT ANALYSIS
Dimensionality reduction
18. CUSTOMER SEGMENTATION
by eigenbehaviormodelling
Eigenbehavior
extraction
ROUTINE
of each user
HOUSEHOLD WATER
CONSUMPTION
Eagle, Nathan, and Alex Sandy Pentland. "Eigenbehaviors: Identifying structure in routine."
Behavioral Ecology and Sociobiology63.7 (2009): 1057-1066.
Water consumers
clustering
Clusters
characterization
Users community
profiles
20. CHALLENGES
. Smart metered BIG DATA
-> PCA
. How to extract RELEVANTUSERS PROFILES out of large smart
metered datasets?
-> Eigenbehavior extractionand clustering
. How to characterize these profiles, in order to inform
DEMANDMANAGEMENT?
-> Profiles characterization and factor mapping
21. thank you
August 22-25 Monte Verità, Switzerland.
More Info: www2.idsia.ch/cms/smartwater/
www.smarth2o-fp7.eu
@smartH2Oproject
#SmartH2O
@AndreaCominola
@NRMPolimi
Andrea Cominola
andrea.cominola@polimi.it
Politecnico di Milano
Department of Electronics, Information and Bioengineering